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@@ -31,10 +31,12 @@ with open("whole_data/x_data.json") as f1:
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X_train['uid'] = X_train['uid'].astype(str)
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X_train['type'] = X_train['type'].astype(str)
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X_train['channel'] = X_train['channel'].astype(str)
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+ X_train['mode'] = X_train['mode'].astype(str)
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X_test = pd.DataFrame(x_list[10000:], columns=my_c)
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X_test['uid'] = X_test['uid'].astype(str)
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X_test['type'] = X_test['type'].astype(str)
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X_test['channel'] = X_test['channel'].astype(str)
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+ X_test['mode'] = X_test['mode'].astype(str)
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with open("whole_data/y_data.json") as f2:
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y_list = json.loads(f2.read())
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@@ -42,7 +44,7 @@ with open("whole_data/y_data.json") as f2:
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y_test = np.array(y_list[10000:])
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# 创建LightGBM数据集
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-train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=['uid', 'type', 'channel'])
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+train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=['uid', 'type', 'channel', 'mode'])
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test_data = lgb.Dataset(X_test, label=y_test, reference=train_data)
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# 设置模型的参数
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@@ -58,7 +60,7 @@ params = {
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# 训练模型
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num_round = 100
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-bst = lgb.train(params, train_data, num_round)
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+bst = lgb.train(params, train_data, num_round, valid_sets=[test_data])
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# 预测
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y_pred = bst.predict(X_test, num_iteration=bst.best_iteration)
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罗俊辉